Raman Distributed Temperature Sensing Research Articles

A Novel Approach to Raman Distributed Temperature-Sensing System for Short-Range Applications.

A novel approach to the development of Distributed Temperature-Sensing (DTS) systems based on Raman Scattering in Multimode optical fibers operating at around 800 nm is presented, focusing on applications requiring temperature profile measurement in the range of a few hundreds of meters. In contrast to the standard Raman DTS systems, which aim to shorten the pulse space width as much as possible to improve the precision of measurement, the novel approach studied in this work is based on the use of pulses with a space width that is approximately equal to the distance covered by the fiber under test. The proposed technique relies on numerical post-processing to obtain the temperature profile measurement with a precision of about ±3 °C and a spatial resolution of 8 m, due to the transaction phases of the optical pulses. This solution simplifies the electronic circuit development, also minimizing the required laser peak power needed compared to the typical narrow pulse techniques.

High spatial resolution temperature variation detection in one direction with a fiber solenoid and RDTS system

Limited by the large bottom width of the pulsed light and the large sampling spacing of the data acquisition card in conventional Raman distributed temperature sensing (RDTS) systems, short straight fibers cannot acquire temperature measurements with high spatial resolution and high temperature accuracy in a single temperature variation region (TVR). In contrast, winding sensing fiber on a screwed tube as a fiber solenoid for temperature sensing can indirectly improve the spatial resolution of the system. However, as the radius of the screwed tube decreases, the ability of the screwed tube to improve the spatial resolution of the system will deteriorate. To break this limitation, we innovatively propose a signal processing scheme to first construct a three-dimensional surface of the sensing length of screwed tube-temperature-area value, then obtain the length of the TVR and the area value of the temperature rise curve generated from a single TVR, and finally demodulate the temperature of the TVR in the surface. The experimental results show that the proposed scheme improves the spatial resolution of the conventional screwed tube from 8 cm to 1 cm, and the average temperature accuracy of the three TVRs with the interval and length of the TVRs in the order of centimeters is ± 0.63 °C. The screwed tube can be used for axial temperature distribution measurements of liquids as well as solids, and the proposed method can further expand the application prospects of the RDTS system for continuous measurement of multiple TVRs with lengths and intervals of centimeters in one direction without changing the internal hardware.

Demodulation of temperature with high spatial resolution based on RDTS system

Measurement of temperature along the fiber based on Raman distributed temperature sensing (RDTS) system has been studied and applied broadly. However, the minimum fiber length and temperature change required to correctly obtain the actual temperature variation in periodic regions (PRs) are limited by the systematic spatial resolution of the order of meters. To improve the spatial resolution of temperature measurement, we propose a novel signal processing scheme that firstly associates the position of each region with the relevant sampling points in the temperature rise curve, then combines the theoretically and experimentally derived relation that different intensities can be obtained by sensing temperature with the luminous flux at different locations within the pulsed light, and finally separates the Raman intensity generated by each region from the temperature rise curve of PRs. The experimental results show that the temperature demodulation of five PRs with a length of 0.5 m can be achieved with an average accuracy of ±2.3 °C. The proposed scheme improves the spatial resolution of the conventional RDTS system from 4 m to 0.5 m and provides a new solution for the temperature monitoring of PRs.

Comparison of Raman, Brillouin, and Rayleigh Distributed Temperature Measurements in High-Rate Wells

With the maturity of and demand for fiber-optic sensing technology growing steadily over the last few years across multiple basins, operators are seeking fiber-optic sensing solutions that address the technology challenges associated with the life-of-field monitoring of subsea developments. Single-ended distributed temperature sensing (DTS) measurements have been acquired for decades now, typically using Raman optical time-domain reflectometry (OTDR) on multimode fiber. However, for topside interrogation of subsea completions, Raman DTS performs poorly. This is due to the available optical power budget and the potential wavelength dependency of optical attenuation across multiple connectors and splices comprising the optical subsea infrastructure. Any wavelength-dependent attenuation as the signals pass through connectors, splices, and optical feedthrough systems will generate step changes in the measured Raman DTS temperature profile. Brillouin OTDR can provide a DTS alternative that overcomes these challenges and operates on single-mode fiber. Brillouin OTDR operates with a large dynamic range to measure a wavelength (frequency) shift of the Stokes/anti-Stokes components that is proportional to both strain and temperature. Since downhole cables are manufactured with optical fibers suspended in a gel and with appropriate extra fiber length (EFL), any fiber strain relaxes, and the Brillouin wavelength shift is an absolute temperature measurement. An additional alternative is also explored here. We typically associate coherent Rayleigh OTDR with distributed acoustic sensing (DAS) on single-mode fibers, but low frequencies also contain a relative temperature dependence. The low-pass filtering of DAS data can then be used as a form of Rayleigh DTS with appropriate data processing. In this paper, we report on a comparison of Raman, Brillouin, and Rayleigh DTS simultaneously acquired in the same high-rate producer and injector wells. We validate that, with appropriate cable design, Brillouin DTS can be simultaneously operated on the same single-mode fiber with DAS and can deliver absolute temperature measurements suitable for production analysis. We also use a laboratory experiment to show that Rayleigh DTS provides an accurate measure of temperature changes comparable in precision to a traditional thermocouple. We conclude with a discussion about the implementation of this DAS-DTS solution for sensing subsea completions.

Enhanced Raman Distributed Temperature Sensor Based on Self-Constructed Fully Connected Neural Network

The performance of Raman Distributed Temperature Sensing (RDTS) is greatly limited by the effect of optical and other random noise on the backscattered Anti-Stokes signal. Therefore, it is necessary to study the signal denoising method of the system. Existing methods to improve system performance mainly include improving the system structure and using denoising methods like cumulative averaging, wavelet transform, etc., which still have room for improvement. In this paper, a Fully-Connected-RDTS model based on fully connected neural network is designed to reduce temperature errors and improve spatial positioning accuracy. Compared with traditional denoising algorithms, the maximum temperature error is reduced to 0.53 °C (±0.25 °C) using only 500 averages of the temperature data, which is better than 2.61 °C of 10,000 cumulative averages, and 2.25 °C of wavelet transforms. The predictions for the boundary of the temperature curve are highly consistent with the original data, and the error of spatial positioning accuracy is less than 1 m without blurring the spatial information like cumulative averaging and wavelet transform algorithms. Compared with the results of 10,000 cumulative averages, the detection response time is reduced from 8 s to 1-s while the noise is greatly filtered. The measurement performance of the RDTS system is greatly improved by the Fully-Connected-RDTS model without sacrificing spatial accuracy. And once the model is successfully trained, there is no need to change the parameters of the model, so it could be used for large-scale real-time detection.

24 km High-Performance Raman Distributed Temperature Sensing Using Low Water Peak Fiber and Optimized Denoising Neural Network

Raman distributed optical fiber temperature sensing (RDTS) has been extensively studied for decades because it enables accurate temperature measurements over long distances. The signal-to-noise ratio (SNR) is the main factor limiting the sensing distance and temperature accuracy of RDTS. We manufacture a low water peak optical fiber (LWPF) with low transmission loss to improve the SNR for long-distance application. Additionally, an optimized denoising neural network algorithm is developed to reduce noise and improve temperature accuracy. Finally, a maximum temperature uncertainty of 1.77 °C is achieved over a 24 km LWPF with a 1 m spatial resolution and a 1 s averaging time.

Super Spatial Resolution Raman Distributed Temperature Sensing via Deep Learning

Raman distributed temperature sensing (RDTS) can obtain temperature information along with an optical fiber. Its spatial resolution is defined by the minimum spatial range that can be resolved by the system. The spatial resolution can be improved by reducing the optical pulse width. However, simultaneously, the signal-to-noise ratio is degraded, and higher speed equipment is required. Recently, deconvolution algorithms have been employed to improve spatial resolution without hardware modification. However, conventional deconvolution algorithms have limited effectiveness. Here, we propose and experimentally demonstrate an RDTS super spatial resolution deep convolutional neural network (SSRNet) to improve the spatial resolution with high fidelity. The convolution kernel of the RDTS is estimated from the falling signal at the fiber end rather than using the waveform of pulsed light. Then a modified RDTS model is built to generate a large amount of training data. Through optimizing the structure of SSRNet, the spatial resolution is increased from 4 m to 0.8 m which has reached the limit of the sampling rate of 250 MSa/s. The simulation and experimental results show that the restored results using SSRNet are more accurate with fewer artifacts than the results using a conventional deconvolution algorithm.

Single-ended self-calibration high-accuracy Raman distributed temperature sensing based on multi-core fiber.

Raman distributed temperature sensing (RDTS) obtains the temperature information by measuring the intensities of Raman scattering lights. The anti-stokes only RDTS can avoid the error caused by wavelength-dependent loss and dispersion. However, to eliminate temperature-independent intensity variations, single-wavelength demodulation generally adopts the double-ended detection scheme. This requires two optical fibers or one fiber to be folded into a loop, which is inconvenient in practical applications. Moreover, the temperature accuracy of such a scheme is lower than the conventional single-ended system, so it has not been widely used. Here, we propose and experimentally demonstrate a multi-core fiber (MCF) based RDTS system. A single-ended loop structure is achieved by connecting two cores at the far end of the MCF with a fan-in/fan-out device. By measuring the backscattered anti-stokes lights in the two cores, the results can be self-calibrated to eliminate the influence of temperature-independent light intensity changes. Besides, the results can be improved by averaging the temperatures of the two cores due to the spatial consistency of the MCF. Moreover, to further improve the temperature uncertainty, we employ the one-dimensional denoising convolutional neural network. Finally, a maximum temperature uncertainty of 1.4 °C is achieved over a 10 km MCF with a 3 m spatial resolution.

Faults Diagnosis in Induction Motors Through Thermal Mapping Produced by the RDTS System

In the industry, production lines depend on small and medium-sized electrical machines, and for this reason, continuous monitoring of these machines is important to avoid unexpected interruptions. Excessive rise in temperature during electric motor operation is usually associated with several causes, including voltage unbalance and overload. This article presents the measurements taken using a Raman Distributed Temperature Sensing (RTDS) device with 1 m spatial resolution to identify induction motor (IM) faults. In this regard, an algorithm to improving spatial resolution through signal processing allows measuring lengths of up to 15 cm. Experimental tests showed that it was possible to identify the temperature increase in four slots with an improvement of 60%. In addition, it was possible to detect faults in conditions of unbalanced voltage through temperature measurements. The proposed method shows that the measurements acquired by the distributed sensors installed in the IM stator allow thermal mapping of the machine’s entire structure.

Enhanced Temperature Measurement Performance: Fusing DTS and DAS Results

This paper presents the fusion of temperature measurements from different fiber sensing technologies. The method benefits from the absolute temperature accuracy of Raman-Distributed Temperature Sensing (DTS), but also the low noise properties, fast response, and high sensitivity to temperature gradients of coherent Rayleigh backscatter based Distributed Acoustic Sensing (DAS), which is processed to Distributed Temperature Gradient Sensing (DTGS) signals. The specific properties of the technologies allow two fundamental concepts, which are outlined in this paper. The concept having a DTS-like behavior, called enhanced-DTS (eDTS), is implemented using Kalman filters, where the fused temperature result yields an improvement of more than factor 3 in temperature resolution compared to DTS only. This allows more accurate absolute temperature results and faster response to temperature events especially at long distances.

Raman distributed temperature sensing for end winding of high‐power generator

The temperature on the busbars of electric generators in the end-winding region needs to be monitored continuously. High temperature is associated with curvatures, seams, and poor ventilation. However, this solution has limitations, such as the number of measurement points. This article presents a reconstruction algorithm to estimate high-resolution signals, from readings of a distributed temperature system for monitoring high-power generator bars. The purpose of this work is to measure the temperature in the end-winding region with optical sensors based on Raman distributed temperature sensing (RDTS). The ability of RDTS equipment to perform a measurement is limited by its spatial resolution of 1 m. The proposed algorithm reduces spatial resolution through signal processing without changing the equipment hardware. Through the experimental results, it was possible to perform accurate temperature measurements for lengths up to 15 cm using the sensor element. With this method, the final winding of a generator bar of a 355 MW hydroelectric plant was instrumented in the laboratory. The results show that it is possible to monitor the temperature over this region, which has a length of 45 cm. Therefore, the proposed method has the potential for the use of RDTS systems for temperature acquisition of small, medium, and large electrical machines.

High-Performance Raman Distributed Temperature Sensing Powered by Deep Learning

Raman-based distributed temperature sensing (RDTS) can achieve temperature measurement of tens of kilometers and is widely used in temperature monitoring of crucial facilities. The accuracy of RDTS is highly determined by the signal-to-noise ratio (SNR) of the acquired spontaneous Raman scattering (SpRS) signals, which is about 60 dB weaker than the pump pulse. Therefore, the performance of single-mode fiber (SMF) based conventional RDTS is poor. To improve its performance, many methods have been proposed, including the use of special optical fibers, pulse coding techniques, and denoising algorithms. However, these methods have their limitations. Here, we propose and experimentally demonstrate a deep 1-D denoising convolutional neural network (1DDCNN) to enhance the performance of RDTS. A simplified RDTS model is built to train and optimize the 1DDCNN. To verify the performance of the 1DDCNN on actual data, we experimentally measure the SpRS data of a 10-km SMF with an average time of 1 s and a spatial resolution of 3 m. The well-trained 1DDCNN reduces the temperature uncertainty from 6.4 °C to 0.7 °C with high fidelity. As a comparison, the commonly used wavelet denoising algorithm can only reduce it to 3.7 °C. Besides, the 1DDCNN does not require manual parameter adjustment and is not affected by the sampling rate, giving it significant advantages in practical applications compared with conventional denoising algorithms. Moreover, we believe that the proposed 1DDCNN can be applied to other distributed optical fiber sensing systems by fine-tuning the network with appropriate training data.

Monitoring Large Railways Infrastructures Using Hybrid Optical Fibers Sensor Systems

In this paper we propose a hybrid fiber optics sensor system, based on Fiber Bragg Gratings (FBG) and Raman distributed temperature sensing (RDTS), for monitoring essential sites within large railway infrastructures like bridges, viaducts and slopes potentially subject to landslides. The geometrical rail track condition is monitored in real-time using fiber optic sensors, providing early warning if pre-defined thresholds are exceeded in terms of longitudinal, horizontal and cross-level defects. The sensor reading units can be remotely located from the monitored zones, without requiring active elements on the tracks and ensuring high immunity to electromagnetic interference. The proposed technology has been first validated in railway laboratories and then in field trials confirming its capability to detect defects over pre-established thresholds and the absence of false alarms with regular train circulation.

RDTS-Based Two-Dimensional Temperature Monitoring with High Positioning Accuracy Using Grid Distribution.

A novel two-dimensional (2D) positioning method based on Raman distributed temperature sensing (RDTS) has been reported to dramatically improve positioning accuracy. Using a well-designed 2D distribution of optical fiber and corresponding algorithms, the heat source can be accurately located without crosstalk; however, there is a tradeoff between sensing distance and positioning accuracy. In our experiments, an RDTS system with a spatial resolution of 0.8 m along a 3 km multimode fiber (MMF) is used with specific 2D routing rules and corresponding algorithms. A positioning accuracy of about 0.1 m is obtained without hardware modification, which could be improved through the dense arrangement of fiber; however, this would sacrifice the sensing length. This solution can be used for both flat surfaces and curved surfaces such as pipes or tank surfaces. This scheme can also be extended to three-dimensional positioning using a delicate routing design of sensing fiber.

Combined distributed Raman and Bragg fiber temperature sensing using incoherent optical frequency domain reflectometry

Abstract. Optical temperature sensors offer unique features which make them indispensable for key industries such as the energy sector. However, commercially available systems are usually designed to perform either distributed or distinct hot spot temperature measurements since they are restricted to one measurement principle. We have combined two concepts, fiber Bragg grating (FBG) temperature sensors and Raman-based distributed temperature sensing (DTS), to overcome these limitations. Using a technique called incoherent optical frequency domain reflectometry (IOFDR), it is possible to cascade several FBGs with the same Bragg wavelength in one fiber and simultaneously perform truly distributed Raman temperature measurements. In our lab we have achieved a standard deviation of 2.5 K or better at a spatial resolution in the order of 1 m with the Raman DTS. We have also carried out a field test in a high-voltage environment with strong magnetic fields where we performed simultaneous Raman and FBG temperature measurements using a single sensor fiber only.

Raman DTS Based on OTDR Improved by Using Gain-Controlled EDFA and Pre-Shaped Simplex Code

This paper described a distributed temperature sensing (DTS) system based on Raman scattering using pre-shaped simplex code (S code) with erbium-doped fiber amplifiers (EDFAs) transient effects suppression techniques. We investigated these techniques to mitigate the waveform distortion caused by the EDFA when DTS uses S code in an optical time-domain reflectometer (OTDR). We tested the DTS experimentally using two EDFA’s transient effects suppression techniques: automatic gain control and pre-shaped S code pulses. The performance of these two techniques working simultaneously was compared with the standard S code technique. In the experimental setup, we tested code word lengths from 3 to 127 b with 100-ns pulsewidth. The temperature measurement demonstration was done in reaches of 40 and 50 km, and in a repeated system of 62 km using standard single-mode fiber and commercial OTDR and EDFA modules, which were properly adapted to this application. Results show that EDFA’s transient effects suppression techniques allow increasing the reach of DTS using short S code sequences, reducing the complexity and the processing time.

Raman Distributed Temperature Sensing at CERN

A field trial has been performed at the CERN high-energy accelerator-mixed (CHARM) field facility, newly developed for testing devices within accelerator representative radiation environments, to validate the use of Raman-based optical fiber sensors for distributed temperature measurements in highly radiative environments. Experimental results demonstrate that Raman distributed temperature sensors, operating in loop configuration on radiation-tolerant optical fibers, are able to compensate for wavelength-dependent losses and are, therefore, robust to harsh environments, in which a mixed-field radiation, including protons, neutrons, photons, and other particles, is potentially altering the fiber material properties. The temperature profile measured on commercial radiation-tolerant optical fibers shows that a temperature resolution <1 °C, 0.5-m spatial resolution, is highly reliable and sets the first step toward a distributed measurement system able to monitor the temperature at the CERN’s large hadron collider for safety purposes. Such a system will also be helpful in correcting the radiation-induced attenuation temperature dependence in distributed radiation sensing systems based on radiation-sensitive optical fibers.

Ambient condition desensitization of a fiber Raman temperature sensing system based on a dynamic sampling-correction scheme.

One of the most important performance indicators of a fiber optic distributed temperature sensing (DTS) system is its operational stability, which depends on the performance of each device making up the system. Such individual device performance itself is affected by temperature and other environmental conditions. As a result, ambient fluctuations invariably lead to measurement uncertainties and drifts, seriously degrading the system performance. To ameliorate such adverse effects of the environment, we propose a dynamic sampling-correction scheme, which we have implemented on our fiber optic Raman DTS system, resulting in an average temperature error within 1°C when the main chassis undergoes a temperature variation over the range -25°C-45°C. The drastically improved system stability foreshadows better performance and reliability in engineering applications.

Reducing Intervention in Subsea Wells With Fiber-Optic Technology

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 23929, ’Reducing Intervention in Subsea Wells With Fiber-Optic Technology,’ by John Lovell, Schlumberger, prepared for the 2013 Offshore Technology Conference, Houston, 6-9 May. The paper has not been peer reviewed. Fiber-optic-system installations have reduced the need for intervention by logging tools and have given crucial insights into wellbore integrity and reservoir production. Consequently, a prime application of fiber technology should be in deepwater fields, where intervention can be prohibitively expensive. The reluctance to incorporate downhole fiber appears to be caused by limitations of technology relating to cables, deployment, acquisition, and interfacing. These barriers are slowly being eliminated, and a small number of subsea wells have now been implemented with in-well fiber. Introduction Permanently installed fiber-optic systems have been commercially available in the oil field since the early 1990s, with the bulk of the deployments providing fiber-optic distributed temperature based upon Raman backscattering. This Raman distributed-temperature-sensing (DTS) measurement system provides continuous data along the length of the completion without the need for intervention and without introducing restrictions in the inner diameter of the completion tubulars, restrictions that could impede subsequent wellbore access. Typical DTS spatial resolution is better than one data point per meter, with a temperature resolution of 0.01°C. Applications of the distributed-temperature data have included calculating flow contributions across the sandface, evaluating water-injection profiles, diagnosing the effectiveness of fracture jobs, finding cement tops behind casing, and identifying crossflow between producing zones. The growth of fiber installations for dry-tree (land and platform) applications is increasing exponentially. More than 1,200 installations have been deployed by one service company alone. Most of those installations were single-stage completions, often for thermal or steam-assisted-gravity-drainage (SAGD) application, and relied upon the ability to pump fiber along a control line to convey the sensing fiber. Approximately 20 of those wells have fiber pumped along the sandface of dual-stage openhole-gravel-pack (OHGP) completions. A significant advantage of the pumped fiber was that, if the fiber degraded over time, it could be pumped out and replaced. This was particularly important before the discovery of how to construct fibers with some immunity to darkening from H+ and OH− ions. With hydrogen resistance came the possibility of increased longevity for fixed-fiber cables, and, since 2006, an alternative to pumped fiber has been available that combines multiple hydrogen-resistant fibers with an electronic line to create a hybrid cable (Fig. 1). This cable can power high-accuracy quartz gauges and can also give the benefits of fully distributed Raman DTS. More than 60 such systems have been deployed to date.

Optimized design on multimode fiber with enhanced spontaneous Raman scattering for distributed temperature sensing

Raman distributed temperature sensing (RDTS) is widely used in many fields. The system configuration, devices, and data processing methods have been well studied and improved, but research on sensing fiber itself is rare. We design a multimode fiber for RDTS based on experimental results from a series of conventional multimode fibers. The optimized multimode fiber offers temperature sensing at a distance less than 10 km. It has a much larger nonlinear effect than conventional multimode fibers, so the spontaneous anti-stokes signal under the same optical input is 1.5 to ∼ 2.4 times higher. Its loss and dispersion within the wavelength range from 1400 to 1650 nm is acceptable. Therefore, under the same system configuration, a higher anti-stokes signal is received by the data acquisition card, which makes signal acquisition and processing easier and improves temperature sensitivity, accuracy, and resolution without sacrificing other characteristics, such as spatial resolution and measuring distance.